def main():
agent_dict = []
errors = []
wall1 = MovingCube(1)
print('began running at %s' % datetime.datetime.now().strftime("%a, %d %B %Y %H:%M:%S"))
for i in range(1):
learner = NeuralNetwork(cru.AGENT_LEARNER_NETWORK_SHAPE, cru.linear_relu, min=-0.1, max=0.1)
curious_agent = CuriousAgent(0)
curious_agent.learner = learner
#sqv.set_global('AGENTS_COUNT', 1)
d = activate_agent(10, 1000, render=False, print_info=False, reset_env=False, reset_agent=True,
agents=[curious_agent, wall1], init_learners=[deepcopy(learner)]*2, get_last_step_avg_error=True,
moving_walls_amount=1, moving_wall_start_index=1)
agent_dict.append(get_agent_dict(d))
print('finished running #%i at %s' % (i + 1, datetime.datetime.now().strftime("%a, %d %B %Y %H:%M:%S")))
agent_dict = join_dict_list(agent_dict)
fig1, ax1 = plot_together(np.arange(len(agent_dict['last_errors'])), [agent_dict['last_errors'],{'label':'curious', 'color':'blue'}],
title='Total Errors STD', axis_labels=['epoch', 'last error'])
plt.show()
from IPython import embed
embed()
def main():
agent_dict = []
random_dict = []
random_agent = RandomAgent(0)
wall1, wall2 = MovingCube(1), MovingCube(2)
print('began running at %s' %
datetime.datetime.now().strftime("%a, %d %B %Y %H:%M:%S"))
color_map_agent = []
sqv.set_global("AGENTS_COUNT", NUMBER_OF_AGENTS)
for i in range(1):
learner = NeuralNetwork(cru.AGENT_LEARNER_NETWORK_SHAPE,
cru.linear_relu,
min=-0.01,
max=0.01)
curious_agent = CuriousAgent(0)
curious_agent.learner = deepcopy(learner)
d = activate_agent(MAX_STEPS,
number_of_epoches=NUM_OF_EPOCHES,
render=False,
print_info=False,
reset_env=False,
agents=[curious_agent],
get_avg_errors=False,
get_values_field=True,
number_of_error_agents=1)
curious_agent.learner = deepcopy(learner)
d1 = activate_agent(MAX_STEPS,
number_of_epoches=1,
render=False,
print_info=False,
reset_env=False,
agents=[curious_agent],
get_avg_errors=True,
get_values_field=True,
number_of_error_agents=1)
d['total_errors'] = d1['total_errors']
agent_dict.append(get_agent_dict(d))
color_map_agent.append(stats.get_color_map(curious_agent))
random_agent.learner = learner
d = activate_agent(MAX_STEPS,
render=False,
print_info=False,
reset_env=False,
agents=[random_agent],
get_avg_errors=True,
number_of_error_agents=1)
random_dict.append(get_agent_dict(d))
print(
'finished running #%i at %s' %
(i + 1, datetime.datetime.now().strftime("%a, %d %B %Y %H:%M:%S")))
means_curious = []
for i in agent_dict:
means_curious.append(i['total_errors'])
std_agent = np.array(means_curious).std(axis=0)
color_map_agent = np.array(color_map_agent).mean(axis=0)
figs, axes = draw_color_maps(color_map_agent)
means_random = []
for i in random_dict:
means_random.append(i['total_errors'])
std_random = np.array(means_random).std(axis=0)
agent_dict = join_dict_list(agent_dict)
#draw_plots(agent_dict)
random_dict = join_dict_list(random_dict)
#draw_plots(random_dict)
#fig, ax ,q = plot_field(*agent_dict['fields'], title='Agent Value Field', color=agent_dict['fields_colors'])
errors_rate_curious = agent_dict['total_errors']
errors_rate_random = random_dict['total_errors']
last_td_agent = np.zeros((len(agent_dict['epoches_tds']), ))
for i, v in enumerate(agent_dict['epoches_tds']):
last_td_agent[i] = v[-1]
fig, ax = plot_together(
np.arange(len(last_td_agent)),
[last_td_agent, {
'label': 'curious',
'color': 'blue'
}],
title='Epochs Last TD',
axis_labels=['epoch', 'last TD'])
fig1, ax1 = plot_together(
random_dict['timesteps'],
[errors_rate_curious, {
'label': 'curious',
'color': 'blue'
}], [errors_rate_random, {
'label': 'random',
'color': 'red'
}],
title='Total Errors STD',
std=[std_agent, std_random],
axis_labels=['steps', 'total error'])
fig2, ax2 = plot_together(
random_dict['timesteps'],
[errors_rate_curious, {
'label': 'curious',
'color': 'blue'
}], [errors_rate_random, {
'label': 'random',
'color': 'red'
}],
title='Total Errors Means',
means=[means_curious, means_random],
axis_labels=['steps', 'total error'])
fig3, ax3 = plot_together(random_dict['timesteps'][:-1], [
stats.derivative(errors_rate_curious), {
'label': 'curious',
'color': 'blue'
}
], [
stats.derivative(errors_rate_random), {
'label': 'random',
'color': 'red'
}
],
title='Total Errors Derivative',
axis_labels=['steps', 'total error'])
fig1.savefig('./plots/std.png')
fig2.savefig('./plots/means.png')
plt.show()